Author name cluster

O. Burçhan Bayazit

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

1 author row

IROS Conference 2009 Conference Paper

Intelligent distributed architecture (IDA) for mobile sensor data fusion

John L. Meier
O. Burçhan Bayazit

In this paper, we provide a new two-layer scalable architecture, Intelligent Distributed Architecture (IDA). The first layer of IDA, the application layer, is responsible for the mission specific tasks. The second layer, the network layer, is responsible for relaying the information quickly to reduce latency. In order to increase efficiency, we propose an intelligent network layer that evaluates the network traffic through application provided policies. The evaluation assesses improved accuracy in bandwidth limited scenarios. IDA policies permit the application to drop, merge or modify the packets in real time from different sources. We demonstrate how this architecture can be applied to an example application, distributed target tracking. We also propose some new algorithms that can be used in conjunction with IDA for target tracking. Our experiments on this application show that IDA improves system performance when the bandwidth is limited.

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ICRA Conference 2007 Conference Paper

Adaptive Embedded Roadmaps For Sensor Networks

Gazihan Alankus
Nuzhet Atay
Chenyang Lu 0001
O. Burçhan Bayazit

In this paper, we propose a new approach to wireless sensor network assisted navigation while avoiding moving dangers. Our approach relies on an embedded roadmap in the sensor network that always contains safe paths. The roadmap is adaptive, i. e. , it adapts its topology to changing dangers. Mobile robots in the environment use the roadmap to reach their destinations. We evaluated the performance of embedded roadmap both in simulations using realistic conditions and with real hardware. Our results show that the proposed navigation algorithm is better suited for sensor networks than traditional navigation field based algorithms. Our observations suggest that there are two drawbacks of traditional navigation field based algorithms, (i) increased power consumption, (ii) message congestion that can prevent important danger avoidance messages to be received by the robots. In contrast, our approach significantly reduces the number of messages on the network (up to 160 times in some scenarios) while increasing the navigation performance.

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ICRA Conference 2006 Conference Paper

A Motion Planning Processor on Reconfigurable Hardware

Nuzhet Atay
O. Burçhan Bayazit

Motion planning algorithms enable us to find feasible paths for moving objects. These algorithms utilize feasibility checks to differentiate valid paths from invalid ones. Unfortunately, the computationally expensive nature of such checks reduces the effectiveness of motion planning algorithms. However, by using hardware acceleration to speed up the feasibility checks, we can greatly enhance the performance of the motion planning algorithms. Of course, such acceleration is not limited to feasibility checks; other components of motion planning algorithms can also be accelerated using specially designed hardware. A field programmable gate array (FPGA) is a great platform to support such an acceleration. An FPGA is a collection of digital gates which can be reprogrammed at run time, i. e. , it can be used as a CPU that reconfigures itself for a given task. In this paper, we study the feasibility of an FPGA based motion planning processor and evaluate its performance. In order to leverage its highly parallel nature and its modular structure, our processor utilizes the probabilistic roadmap method at its core. The modularity enables us to replace the feasibility criteria with other ones. The reconfigurability lets us run our processor in different roles, such as a motion planning co-processor, an autonomous motion planning processor or dedicated collision detection chip. Our experiments show that such a processor is not only feasible but also can greatly increase the performance of current algorithms

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IROS Conference 2005 Conference Paper

Iterative relaxation of constraints: a framework for improving automated motion planning

O. Burçhan Bayazit
Dawen Xie
Nancy M. Amato

This paper presents a technique for improving the efficiency of automated motion planners. Motion planning has application in many areas such as robotics, virtual reality systems, computer-aided design, and even computational biology. Although there have been steady advances in motion planning algorithms, especially in randomized approaches such as probabilistic roadmap methods (PRMs) or rapidly-exploring random trees (RRTs), there are still some classes of problems that cannot be solved efficiently using these state-of-the-art motion planners. In this paper, we suggest an iterative strategy addressing this problem where we first simplify the problem by relaxing some feasibility constraints, solve the easier version of the problem, and then use that solution to help us find a solution for the harder problem. We show how this strategy can be applied to rigid bodies and to linkages with high degrees of freedom, including both open and closed chain systems. Experimental results are presented for linkages composed of 9-98 links. Although we use PRMs as the automated planner, the framework is general and can be applied with other motion planning techniques as well.

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IROS Conference 2005 Conference Paper

Spatiotemporal query strategies for navigation in dynamic sensor network environments

Gazihan Alankus
Nuzhet Atay
Chenyang Lu 0001
O. Burçhan Bayazit

Autonomous mobile agent navigation is crucial to many mission-critical applications (e. g. , search and rescue missions in a disaster area). In this paper, we present how sensor networks may assist probabilistic roadmap methods (PRMs), a class of efficient navigation algorithms particularly suitable for dynamic environments. A key challenge of applying PRM algorithms in dynamic environment is that they require the spatiotemporal sensing of the environment to solve a given navigation problem. To facilitate navigation, we propose a set of query strategies that allow a mobile agent to periodically collect real-time information (e. g. , fire conditions) about the environment through a sensor network. Such strategies include local spatiotemporal query (query of spatial neighborhood), global spatiotemporal query (query of all sensors), and border query (query of the border of danger fields). We investigate the impact of different query strategies through simulations under a set of realistic fire conditions. We also evaluate the feasibility of our approach using a real robot and real motes. Our results demonstrate that (1) spatiotemporal queries from a sensor network result in significantly better navigation performance than traditional approaches based on on-board sensors of a robot; (2) the area of local queries represent a tradeoff between communication cost and navigation performance; (3) through in-network processing our border query strategy achieves the best navigation performance at a small fraction of communication cost compared to global spatiotemporal queries.

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ICRA Conference 2004 Conference Paper

Shepherding Behaviors

Jyh-Ming Lien
O. Burçhan Bayazit
Ross T. Sowell
Samuel Rodríguez
Nancy M. Amato

Shepherding behaviors are a type of flocking behavior in which outside agents guide or control members of a flock. Shepherding behaviors can be found in various forms in nature. For example, herding, covering, patrolling and collecting are common types of shepherding behaviors. In this work, we investigate ways to simulate these types of behaviors. A shepherd uses roadmaps to steer the flock and to re-group separated flock members. This paper focuses on improving the shepherd's movements to gain better control of the flock's motion and use this improved control to demonstrate a wider variety of shepherding behaviors.

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ICRA Conference 2002 Conference Paper

Probabilistic Roadmap Motion Planning for Deformable Objects

O. Burçhan Bayazit
Jyh-Ming Lien
Nancy M. Amato

In this paper, we investigate methods for motion planning for deformable robots. Our framework is based on a probabilistic roadmap planner. As with traditional motion planning, the planner's goal is to find a valid path for the robot. Unlike typical motion planning, the robot is allowed to change its shape (deform) to avoid collisions as it moves along the path. We propose a two-stage approach. First, an 'approximate' path which may contain collisions is found. Next, we attempt to correct any collisions on this path by deforming the robot. We propose and analyze two methods for performing the deformations. Both techniques are inspired by a physically correct behavior, but are more efficient than completely, physically correct methods. Our approach can be applied in several domains, including flexible robots, computer modeling and animation, and biological simulations.

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ICRA Conference 2001 Conference Paper

Ligand Binding with OBPRM and User Input

O. Burçhan Bayazit
Guang Song
Nancy M. Amato

We present a framework for studying ligand binding which is based on techniques recently developed in the robotics motion planning community. We are interested in locating binding sites on the protein for ligand molecule. Our work investigates the performance of a fully automated motion planner, as well as the effects of supplementary user input collected using a haptic device. Our results applying an obstacle-based probabilistic roadmap motion planning algorithm (OBPRM) to some protein-ligand complexes are encouraging. The framework successfully identified potential building sites for all complexes studied. We find that user input helps the planner, and haptic device helps the user to understand the protein structure by enabling them to feel the difficult-to-visualize forces.

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ICRA Conference 2000 Conference Paper

Enhancing Randomized Motion Planners: Exploring with Haptic Hints

O. Burçhan Bayazit
Guang Song
Nancy M. Amato

We investigate methods for enabling a human operator and an automatic motion planner to cooperatively solve a motion planning query. Our goal is to develop techniques by which the automatic planner can utilize (easily generated) user-input, and determine 'natural' ways to inform the user of the progress made by the motion planner. We show that simple randomized techniques inspired by probabilistic roadmap methods are quite useful for transforming approximate, user-generated paths into collision-free paths, and describe an iterative transformation method which enables one to transform a solution for an easier version of the problem into a solution for the original problem. We also illustrate that simple visualization techniques can provide meaningful representations of the planner's progress in a 6-dimensional C-space. We illustrate the utility of our methods on difficult problems involving complex 3D CAD models.

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ICRA Conference 1998 Conference Paper

Choosing Good Distance Metrics and Local Planners for Probabilistic Roadmap Methods

Nancy M. Amato
O. Burçhan Bayazit
Lucia K. Dale
Christopher Jones
Daniel Vallejo

This paper presents a comparative evaluation of different distance metrics and local planners within the content of probabilistic roadmap methods for motion planning. Both C-space and workspace distance metrics and local planners are considered. The study concentrates on cluttered 3D workspaces, typical of mechanical designs. Our results include recommendations for selecting appropriate combinations of distance metrics and local planners for use in motion planning methods, particularly probabilistic roadmap methods. We find that each local planner makes some connections than none of the others do ndicating that better connected roadmaps will be constructed using multiple local planners. We propose a new local planning method, we call rotate-at-s, that outperforms the common straight-line in C-space method in crowded environments.

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